Setup

Load packages

library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(scales) # scales on plots
library(lme4) # for linear mixed models
library(psych) # for factor analysis

R / package versions used

R.Version() 
## $platform
## [1] "x86_64-w64-mingw32"
## 
## $arch
## [1] "x86_64"
## 
## $os
## [1] "mingw32"
## 
## $system
## [1] "x86_64, mingw32"
## 
## $status
## [1] ""
## 
## $major
## [1] "4"
## 
## $minor
## [1] "0.5"
## 
## $year
## [1] "2021"
## 
## $month
## [1] "03"
## 
## $day
## [1] "31"
## 
## $`svn rev`
## [1] "80133"
## 
## $language
## [1] "R"
## 
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
## 
## $nickname
## [1] "Shake and Throw"
packageVersion('here')
## [1] '1.0.1'
packageVersion('tidyverse')
## [1] '1.3.1'
packageVersion('scales') 
## [1] '1.1.1'
packageVersion('lme4') 
## [1] '1.1.27.1'
packageVersion('psych')
## [1] '2.1.6'

Read pre-wrangled data

(see 00-wrangling-setup.Rmd script)

# here::here()
apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)
## # A tibble: 6 x 46
##   session    group ppt   status   selected continue L1_bsl prior_BSL age_s1
##   <fct>      <fct> <fct> <fct>       <dbl>    <dbl>  <dbl>     <dbl>  <dbl>
## 1 pre-degree pilot HW002 rejected        0        0     NA        NA     NA
## 2 pre-degree pilot HW004 rejected        0        0     NA        NA     NA
## 3 pre-degree pilot HW009 rejected        0        0     NA        NA     NA
## 4 pre-degree pilot HW011 rejected        0        0     NA        NA     NA
## 5 pre-degree pilot HW012 rejected        0        0     NA        NA     NA
## 6 pre-degree pilot HW014 rejected        0        0     NA        NA     NA
## # ... with 37 more variables: nback_lett <dbl>, nback_spat <dbl>,
## #   nback_comb <dbl>, corsi_bspan <dbl>, corsi_score <dbl>, corsi_corr <dbl>,
## #   corsi_mspan <dbl>, kirk_ceil <dbl>, kirk_raw <dbl>, kirk_acc <dbl>,
## #   kbit_ceil <dbl>, kbit_raw <dbl>, kbit_acc <dbl>, dspan_mem <dbl>,
## #   dspan_corr <dbl>, dspan_time <dbl>, mr2d_acc <dbl>, mr2d_rt <dbl>,
## #   mr2d_sats <dbl>, mr3d_acc <dbl>, mr3d_rt <dbl>, mr3d_sats <dbl>,
## #   bis_tot <dbl>, bis_att <dbl>, bis_mot <dbl>, bis_nplan <dbl>, ...

Filter out participants who did not progress beyond interview

apt <- apt %>% filter(status != "rejected")

Convert data from long format to wide format

apt_wide <- apt %>% 
  select(-comments) %>% 
  tidyr::pivot_wider(names_from = session,
                     values_from = c(nback_lett:grade_terp))

Plots & correlations

BSL: predicted impact

Corsi Blocks

vs BSL Grades
# Initial visuospatial skill vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Initial visuospatial skill vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000122
# Initial visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "Initial visuospatial skill vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000427
# 1st year visuospatial skill vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "1st year visuospatial skill vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "1st year Corsi Blocks score ")

apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.129
# 1st year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "1st year visuospatial skill vs. 2nd year BSL grades", 
       y = "1st year BSL grade", 
       x = "2nd year Corsi Blocks score ")

apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000223
# 2nd year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year visuospatial skill vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year Corsi Blocks score ")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00871
vs BSL-SRT

Does Corsi Blocks performance relate to BSL Sentence Reproduction Task?

# Initial Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "Initial Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.101
# 2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "2nd year Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.609
# 3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year Corsi Blocks score")

apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.301

2D Mental Rotation

vs BSL Grades

Does 2D Mental Rotation performance relate to BSL grades?

# Initial 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .3) +
  theme_minimal() +
  labs(title = "Initial 2D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.347
# Initial 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .3) +
  geom_point() +
  theme_minimal() +
  labs(title = "Initial 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00131
# 1st year 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .3) +
  theme_minimal() +
  labs(title = "1st year 2D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "1st year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.664
# 1st year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .3) +
  theme_minimal() +
  labs(title = "1st year 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "1st year BSL grade", 
       x = "2nd year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.119
# 2nd year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .3) +
  theme_minimal() +
  labs(title = "2nd year 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.265
vs BSL-SRT

Does 2D Mental Rotation performance relate to BSL Sentence Reproduction Task?

# 2nd year 2D Mental Rotation score vs. 2nd year BSL-SRT scores
# just 4 data points here

# 3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year 2D Mental Rotation score")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.191

3D Mental Rotation

vs BSL Grades

Does 3D Mental Rotation performance relate to BSL grades?

# Initial 3D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Initial 3D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0671
# Initial 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "Initial 3D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000282
# 2nd year 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year 3D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.386
vs BSL-SRT

Does 3D Mental Rotation performance relate to BSL Sentence Reproduction Task?

# Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.369
# 2nd year 3D Mental Rotation score vs. 2nd year BSL-SRT scores
# only 4 data points here

# 3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year 3D Mental Rotation score")

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.123

MLAT Number Learning

vs BSL Grades
# Initial MLAT Number Learning Accuracy vs. BSL grades 1st year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`grade_bsl_1st year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 1st year", 
       x = "MLAT Number Learning Accuracy")

apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0215
# Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`grade_bsl_2nd year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year", 
       x = "MLAT Number Learning Accuracy")

apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0333
vs BSL-SRT
# Initial MLAT Number Learning Accuracy vs. BSL-SRT 3rd year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`bsl_srt_3rd year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL-SRT score 3rd year", 
       x = "MLAT Number Learning Accuracy")

apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000354

BSL: no predicted impact

Kirklees Sentence Reading

vs BSL Grades
# Kirklees Sentence Reading pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 1st year", y = "1st year BSL grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.187
# Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point() +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0116
# Kirklees Sentence Reading 1st year vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Kirklees Sentence Reading after 1 year")

apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.116
# Kirklees Sentence Reading 1st year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading after 1 year")

apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00827
# Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00824
vs BSL-SRT

Does Kirklees Sentence Reading performance relate to BSL Sentence Reproduction Task?

# Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial Kirklees Sentence Reading score")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.237
# 2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year Kirklees Sentence Reading score")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.149

KBIT-2 Matrices

vs BSL Grades
# KBIT-2 Matrices pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0140
# KBIT-2 Matrices pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00126
# KBIT-2 Matrices 1st year vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "KBIT-2 Matrices 1st year")

apt_wide %>% 
  filter(`kbit_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00195
# KBIT-2 Matrices 1st year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices 1st year")

apt_wide %>% 
  filter(`kbit_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0305
# KBIT-2 Matrices 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 2nd year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.167
vs BSL-SRT

Does KBIT-2 Matrices performance relate to BSL Sentence Reproduction Task?

# Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial KBIT-2 Matrices score")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00494
# 2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year KBIT-2 Matrices score")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0933

Dual N-Back

vs BSL Grades
# Dual N-Back pre-degree vs. BSL Grades 1st year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back pre-degree vs. BSL Grades 1st year", 
       y = "1st year BSL grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0288
# Dual N-Back pre-degree vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point() +
  theme_minimal() +
  labs(title = " Dual N-Back pre-degree vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00637
# Dual N-Back 2nd year vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back 2nd year vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0520
vs BSL-SRT

Does Dual N-Back performance relate to BSL Sentence Reproduction Task?

# Initial Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "Initial Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial Dual N-Back score")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00186
# 2nd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "2nd year Dual N-Back score")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.218
# 3rd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3rd year Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year Dual N-Back score")

apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.122

Digit Span

vs BSL Grades
# Digit Span pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.122
# Digit Span pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "Digit Span pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.141
# No datapoints for Digit Span @ '1 year of study'

# Digit Span 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span 2nd year vs BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Digit Span 2nd year")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0796
vs BSL-SRT

Does Digit Span score relate to BSL-SRT score?

# Initial Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Initial Digit Span score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial Digit Span score")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00124
# 2nd year Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2nd year Digit Span score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year Digit Span score")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00433

Barratt Impulsiveness Scale

vs BSL Grades
# Barratt Impulsiveness Scale 2nd year vs BSL grades 2nd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_bsl_2nd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Impulsivity vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "BIS 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00858
vs BSL-SRT
# Barratt Impulsiveness Scale 2nd year vs BSL-SRT 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`bsl_srt_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Impulsivity 2nd year vs. BSL-SRT 3rd year", 
       y = "3rd year BSL-SRT score", x = "BIS Score 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.272

Interpreting: predicted impact

Dual N-Back

vs Interpreting Grades
# Dual N-Back pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0123
# Dual N-Back pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point() +
  theme_minimal() +
  labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.108
# Dual N-Back 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000705
vs Eng>BSL interpreting
# Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.125
# Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.121
# Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 3rd year")

apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.155
vs BSL>Eng interpreting
# Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back pre-degree")

apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0605
# Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 2nd year")

apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.178
# Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = " Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 3rd year")

apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.103

Corsi Blocks

vs Interpreting Grades
# Corsi Blocks pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0114
# Corsi Blocks pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00282
# Corsi Blocks 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Corsi Blocks 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks 1st year")

apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.118
# Corsi Blocks 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Corsi Blocks 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks 2nd year")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0791
vs Eng>BSL interpreting
#  Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = " Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.289
#  Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = " Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 2nd year")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.565
#  Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = " Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 3rd year")

apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.132
vs BSL>Eng interpreting
#  Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = " Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks pre-degree")

apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0578
#  Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = " Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 2nd year")

apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.455
#  Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = " Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 3rd year")

apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0169

Digit Span

vs Interpreting Grades
# Digit Span pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Digit Span pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0155
# Digit Span pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point() +
  theme_minimal() +
  labs(title = "Digit Span pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.338
# Digit Span 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Digit Span 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0709
vs Eng>BSL interpreting
# Digit Span pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0926
# Digit Span 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span 2nd year")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0851
# Digit Span 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span 3rd year")

apt_wide %>% 
  filter(`dspan_corr_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.115
vs BSL>Eng interpreting
# Digit Span pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span pre-degree")

apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00697
# Digit Span 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span 2nd year")

apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0863
# Digit Span 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Digit Span 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span 3rd year")

apt_wide %>% 
  filter(`dspan_corr_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0264

Interpreting: no predicted impact

Kirklees Sentence Reading

vs Interpreting Grades
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.160
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point() +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0931
# Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading after 1 year")

apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0504
# Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() +
  labs(title = "Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0457
vs Eng>BSL interpreting
# Kirklees pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Kirklees pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Kirklees pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0754
# Kirklees 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Kirklees 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Kirklees 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0193
vs BSL>Eng interpreting
# Kirklees pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Kirklees pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Kirklees pre-degree")

apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0922
# Kirklees 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "Kirklees 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Kirklees 2nd year")

apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000130

KBIT-2 Matrices

vs Interpreting Grades
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0419
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0250
# KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 1st year")

apt_wide %>% 
  filter(`kbit_acc_1st year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00539
# KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.116
vs Eng>BSL interpreting
# KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000458
# KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000597
vs BSL>Eng interpreting
# KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")

apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0417
# KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")

apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0172

2D Mental Rotation

vs Interpreting Grades
# 2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title = "2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0382
# 2D Mental Rotation 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2D Mental Rotation 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation 1st year")

apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.325
# 2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.150
vs Eng>BSL interpreting
# 2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.198
# 2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 3rd year")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.180
vs BSL>Eng interpreting
# 2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.602
# 2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.311

3D Mental Rotation

vs Interpreting Grades
# 3D Mental Rotation pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "3D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0677
# 3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point() +
  theme_minimal() +
  labs(title= "3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "3D Mental Rotation pre-degree") 

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.182
# 3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
       y = "2nd year Interpreting grade", x = "3D Mental Rotation 2nd year") 

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.261
# 3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
       y = "2nd year Interpreting grade", x = "3D Mental Rotation 3rd year") 

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.435
vs Eng>BSL interpreting
# 3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.268
# 3D Mental Rotation 2nd year vs. Eng to BSL interpreting  3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0974
# 3D Mental Rotation 3rd year vs. Eng to BSL interpreting  3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 3rd year")

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00752
vs BSL>Eng interpreting
# 3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")

apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.475
# 3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.286
# 3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title = "3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")

apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.158

Barratt Impulsiveness Scale

vs Interpreting Grades
# Barratt Impulsiveness Scale 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_terp_2nd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Impulsivity 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "BIS score 2nd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000311
vs Eng>BSL interpreting
# Barratt Impulsiveness Scale 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`terp_e2b_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Impulsivity 2nd year vs. Eng to BSL interpreting 3rd year", 
       y = "2nd year Interpreting grade", x = "Eng to BSL interpreting 3rd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.115
# Barratt Impulsiveness Scale 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`terp_b2e_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point() +
  geom_smooth(method= "lm") +
  theme_minimal() +
  labs(title= "Impulsivity 2nd year vs. BSL to Eng interpreting 3rd year", 
       y = "2nd year Interpreting grade", x = "BSL to Eng interpreting 3rd year")

apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.236
vs BSL>Eng interpreting

Models

BSL Grades

What best predicts BSL grades?

( bsl_grade_mdl <- lm(grade_bsl ~ nback_comb +
                                  mr2d_sats +
                                  mr3d_sats + 
                                  corsi_corr +
                                  kirk_acc, 
                                  data = apt) ) 
## 
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats + 
##     corsi_corr + kirk_acc, data = apt)
## 
## Coefficients:
## (Intercept)   nback_comb    mr2d_sats    mr3d_sats   corsi_corr     kirk_acc  
##     193.215     -191.407       -1.222        6.259       -1.350       24.318
summary(bsl_grade_mdl)
## 
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats + 
##     corsi_corr + kirk_acc, data = apt)
## 
## Residuals:
##     73     77     78     81     83     85     86     88     89     95 
##  8.583 -7.943 -2.078  1.933  4.007 -3.462  1.239 -5.142  5.770 -2.906 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  193.215    124.141   1.556    0.195
## nback_comb  -191.407    148.647  -1.288    0.267
## mr2d_sats     -1.222      3.894  -0.314    0.769
## mr3d_sats      6.259      3.153   1.985    0.118
## corsi_corr    -1.350      2.861  -0.472    0.662
## kirk_acc      24.318     29.134   0.835    0.451
## 
## Residual standard error: 7.788 on 4 degrees of freedom
##   (122 observations deleted due to missingness)
## Multiple R-squared:  0.6388, Adjusted R-squared:  0.1873 
## F-statistic: 1.415 on 5 and 4 DF,  p-value: 0.3794
# only fit on 10 observations

# wide version - pre-degree
( bsl_grade_mdl_2 <- lm(`grade_bsl_2nd year` ~ 
                                  `nback_comb_pre-degree` +
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree` + 
                                  `mlat_acc_pre-degree`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `mlat_acc_pre-degree`, data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                 19.6328                  13.1204                  -0.3256  
## `corsi_corr_pre-degree`    `kirk_acc_pre-degree`    `mlat_acc_pre-degree`  
##                  1.3124                  20.4016                   7.0848
summary(bsl_grade_mdl_2)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `mlat_acc_pre-degree`, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.728  -4.231   1.410   5.753  20.986 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              19.6328    69.4625   0.283    0.783
## `nback_comb_pre-degree`  13.1204    82.3773   0.159    0.876
## `mr3d_sats_pre-degree`   -0.3256     2.2455  -0.145    0.887
## `corsi_corr_pre-degree`   1.3124     3.3588   0.391    0.703
## `kirk_acc_pre-degree`    20.4016    39.9823   0.510    0.620
## `mlat_acc_pre-degree`     7.0848    17.8677   0.397    0.699
## 
## Residual standard error: 13.72 on 11 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0749, Adjusted R-squared:  -0.3456 
## F-statistic: 0.1781 on 5 and 11 DF,  p-value: 0.9652
# final assessments in 3rd year
( bsl_grade_mdl_3 <- lm(`grade_bsl_2nd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`, 
##     data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##               -43.661                 89.951                  4.507  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                 6.162                  5.814                -22.966
summary(bsl_grade_mdl_3)
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`, 
##     data = apt_wide)
## 
## Residuals:
##      15      17      19      20      22      23      29      31 
##  0.1538  2.5860  0.4291  4.1587 -2.6070  0.9511 -3.4987 -2.1730 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -43.661     49.353  -0.885   0.4697  
## `nback_comb_3rd year`   89.951     83.192   1.081   0.3926  
## `mr3d_sats_3rd year`     4.507      2.526   1.784   0.2163  
## `mr2d_sats_3rd year`     6.162      5.855   1.052   0.4030  
## `corsi_corr_3rd year`    5.814      1.865   3.116   0.0894 .
## `dspan_corr_3rd year`  -22.966     77.087  -0.298   0.7939  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.942 on 2 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.8976, Adjusted R-squared:  0.6414 
## F-statistic: 3.504 on 5 and 2 DF,  p-value: 0.2368

BSL-SRT

( bsl_srt_mdl <- lm(bsl_srt ~ nback_comb + 
                                  mr3d_sats + 
                                  corsi_corr,
                                  #kirk_acc +
                                  #kbit_acc + 
                                  #dspan_corr,
                                  data = apt) ) 
## 
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr, data = apt)
## 
## Coefficients:
## (Intercept)   nback_comb    mr3d_sats   corsi_corr  
##    -46.2392      75.5416       1.8857       0.4374
summary(bsl_srt_mdl)
## 
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr, data = apt)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7754 -3.2740  0.5096  2.4977  5.8657 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -46.2392    27.7600  -1.666   0.1267  
## nback_comb   75.5416    34.6931   2.177   0.0545 .
## mr3d_sats     1.8857     1.1852   1.591   0.1427  
## corsi_corr    0.4374     1.0153   0.431   0.6757  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.009 on 10 degrees of freedom
##   (118 observations deleted due to missingness)
## Multiple R-squared:  0.4738, Adjusted R-squared:  0.3159 
## F-statistic: 3.001 on 3 and 10 DF,  p-value: 0.08167
# only being fit on 8 or 9 observations...

# wide version - pre-degree
( bsl_srt_mdl_2 <- lm(`bsl_srt_3rd year` ~ 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree`, 
                                   data = apt_wide) ) 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + 
##     `kirk_acc_pre-degree`, data = apt_wide)
## 
## Coefficients:
##             (Intercept)   `mr3d_sats_pre-degree`  `corsi_corr_pre-degree`  
##                  -6.040                    1.238                    1.072  
##   `kirk_acc_pre-degree`  
##                  13.862
summary(bsl_srt_mdl_2)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + 
##     `kirk_acc_pre-degree`, data = apt_wide)
## 
## Residuals:
##      15      17      19      20      22      23      24      29      31 
## -0.3363 -2.2854 -2.4169  1.0018  1.4308  2.5661  3.0885 -3.3123  0.2637 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -6.0398    12.7597  -0.473    0.656
## `mr3d_sats_pre-degree`    1.2379     0.9997   1.238    0.271
## `corsi_corr_pre-degree`   1.0715     0.8152   1.314    0.246
## `kirk_acc_pre-degree`    13.8625    10.3682   1.337    0.239
## 
## Residual standard error: 2.877 on 5 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.5861, Adjusted R-squared:  0.3377 
## F-statistic:  2.36 on 3 and 5 DF,  p-value: 0.1881
# final assessments in 3rd year
( bsl_srt_mdl_3 <- lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year`,
                                  #`dspan_corr_3rd year`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year`, data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##              -41.6809                45.3030                -0.2303  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  
##                4.2812                 2.2432
summary(bsl_srt_mdl_3)
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year`, data = apt_wide)
## 
## Residuals:
##       15       17       19       20       22       23       24       29 
##  0.42204 -0.05988 -0.78030  1.07448  0.92041  0.44188  1.39317 -0.48429 
##       31 
## -2.92750 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -41.6809    17.0093  -2.450   0.0704 .
## `nback_comb_3rd year`  45.3030    22.3562   2.026   0.1127  
## `mr3d_sats_3rd year`   -0.2303     0.9437  -0.244   0.8192  
## `mr2d_sats_3rd year`    4.2812     1.5689   2.729   0.0525 .
## `corsi_corr_3rd year`   2.2432     0.5704   3.933   0.0171 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.853 on 4 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.8627, Adjusted R-squared:  0.7253 
## F-statistic: 6.282 on 4 and 4 DF,  p-value: 0.0514

Interpreting Grades

( terp_grade_mdl <- lm(grade_terp ~ nback_spat + 
                                    mr3d_sats + 
                                    corsi_corr +
                                    kirk_acc + 
                                    dspan_corr,
                                    data = apt) )
## 
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr + 
##     kirk_acc + dspan_corr, data = apt)
## 
## Coefficients:
## (Intercept)   nback_spat    mr3d_sats   corsi_corr     kirk_acc   dspan_corr  
##     337.571     -311.756        6.728       -7.193       59.349      -49.499
summary(terp_grade_mdl)
## 
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr + 
##     kirk_acc + dspan_corr, data = apt)
## 
## Residuals:
##       73       77       78       81       83       85       86       88 
##  4.91726  0.34380 -4.86083  2.62055 -3.46552 -2.66947  0.47671 -1.86206 
##       89       95 
##  4.43898  0.06057 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  337.571     79.773   4.232   0.0134 *
## nback_spat  -311.756     87.113  -3.579   0.0232 *
## mr3d_sats      6.728      1.562   4.306   0.0126 *
## corsi_corr    -7.193      2.020  -3.560   0.0236 *
## kirk_acc      59.349     18.432   3.220   0.0323 *
## dspan_corr   -49.499     21.795  -2.271   0.0856 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.933 on 4 degrees of freedom
##   (122 observations deleted due to missingness)
## Multiple R-squared:  0.8833, Adjusted R-squared:  0.7374 
## F-statistic: 6.056 on 5 and 4 DF,  p-value: 0.05278
# but is only being fit on 10 observations.....

# wide version - pre-degree
( terp_grade_mdl_2 <- lm(`grade_terp_2nd year` ~ 
                                  `nback_comb_pre-degree` + 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree` + 
                                  `dspan_corr_pre-degree`,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `dspan_corr_pre-degree`, data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                 -3.6037                  40.0142                   1.5724  
## `corsi_corr_pre-degree`    `kirk_acc_pre-degree`  `dspan_corr_pre-degree`  
##                  1.9683                  -0.5881                  30.7403
summary(terp_grade_mdl_2)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `dspan_corr_pre-degree`, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.520  -1.366   1.526   2.695  10.381 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -3.6037    52.7366  -0.068    0.947
## `nback_comb_pre-degree`  40.0142    60.4054   0.662    0.529
## `mr3d_sats_pre-degree`    1.5724     1.5528   1.013    0.345
## `corsi_corr_pre-degree`   1.9683     3.1280   0.629    0.549
## `kirk_acc_pre-degree`    -0.5881    74.3808  -0.008    0.994
## `dspan_corr_pre-degree`  30.7403    63.5947   0.483    0.644
## 
## Residual standard error: 9.166 on 7 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.5111, Adjusted R-squared:  0.1619 
## F-statistic: 1.463 on 5 and 7 DF,  p-value: 0.3116
# final assessments in 3rd year
( terp_grade_mdl_3 <- lm(`grade_terp_2nd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` + 
##     `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` + 
##     `dspan_corr_3rd year`, data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##               -50.794                 33.848                  4.540  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                11.009                  5.241                 38.905
summary(terp_grade_mdl_3)
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` + 
##     `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` + 
##     `dspan_corr_3rd year`, data = apt_wide)
## 
## Residuals:
##      15      17      19      20      22      23      29      31 
##  1.4516 -4.0283  0.5060  1.8034 -2.5138  1.3605  0.8567  0.5638 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -50.794     39.354  -1.291    0.326  
## `nback_comb_3rd year`   33.848     66.338   0.510    0.661  
## `mr3d_sats_3rd year`     4.540      2.014   2.254    0.153  
## `mr2d_sats_3rd year`    11.009      4.668   2.358    0.142  
## `corsi_corr_3rd year`    5.241      1.488   3.524    0.072 .
## `dspan_corr_3rd year`   38.905     61.470   0.633    0.592  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.941 on 2 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.9379, Adjusted R-squared:  0.7825 
## F-statistic: 6.037 on 5 and 2 DF,  p-value: 0.1482

BSL>Eng interpreting

( terp_b2e_mdl <- lm(terp_b2e ~ nback_spat + 
                                    mr3d_sats + 
                                    corsi_corr +
                                    dspan_corr,
                                    data = apt) )
## 
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr, data = apt)
## 
## Coefficients:
## (Intercept)   nback_spat    mr3d_sats   corsi_corr   dspan_corr  
##      38.330       33.640        1.727        1.172      -13.322
summary(terp_b2e_mdl)
## 
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr, data = apt)
## 
## Residuals:
##     114     116     118     119     121     122     123     130 
## -5.3638  0.7723  1.1635  2.0278 -3.5135 -2.5503  5.7516  1.7124 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   38.330     45.688   0.839    0.463
## nback_spat    33.640     60.825   0.553    0.619
## mr3d_sats      1.727      2.716   0.636    0.570
## corsi_corr     1.172      2.400   0.488    0.659
## dspan_corr   -13.322     56.482  -0.236    0.829
## 
## Residual standard error: 5.468 on 3 degrees of freedom
##   (124 observations deleted due to missingness)
## Multiple R-squared:  0.3151, Adjusted R-squared:  -0.5981 
## F-statistic: 0.3451 on 4 and 3 DF,  p-value: 0.8347
# but is only being fit on 10 observations.....

# wide version - pre-degree
( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~ 
                                  `nback_comb_pre-degree` + 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `dspan_corr_pre-degree`,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`, 
##     data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                 911.589                 -464.608                    1.394  
## `corsi_corr_pre-degree`  `dspan_corr_pre-degree`  
##                  22.437                -1009.482
summary(terp_b2e_mdl_2)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`, 
##     data = apt_wide)
## 
## Residuals:
## ALL 5 residuals are 0: no residual degrees of freedom!
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               911.589         NA      NA       NA
## `nback_comb_pre-degree`  -464.608         NA      NA       NA
## `mr3d_sats_pre-degree`      1.394         NA      NA       NA
## `corsi_corr_pre-degree`    22.437         NA      NA       NA
## `dspan_corr_pre-degree` -1009.482         NA      NA       NA
## 
## Residual standard error: NaN on 0 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:      1,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 4 and 0 DF,  p-value: NA
( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~ 
                                  `mr3d_sats_pre-degree`,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree`, data = apt_wide)
## 
## Coefficients:
##            (Intercept)  `mr3d_sats_pre-degree`  
##                 66.933                   2.461
# final assessments in 3rd year
( terp_b2e_mdl_3 <- lm(`terp_b2e_3rd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`, 
##     data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##               26.0354                57.4582                -0.4345  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                5.3268                 1.7019               -24.8365
summary(terp_b2e_mdl_3)
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`, 
##     data = apt_wide)
## 
## Residuals:
##       15       17       19       20       22       23       24       31 
## -0.50790 -0.02649  2.17263  2.19702 -1.77328 -5.94448  3.33560  0.54691 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            26.0354    58.8754   0.442    0.702
## `nback_comb_3rd year`  57.4582    97.3875   0.590    0.615
## `mr3d_sats_3rd year`   -0.4345     3.4667  -0.125    0.912
## `mr2d_sats_3rd year`    5.3268     4.8509   1.098    0.387
## `corsi_corr_3rd year`   1.7019     2.4483   0.695    0.559
## `dspan_corr_3rd year` -24.8365    60.1614  -0.413    0.720
## 
## Residual standard error: 5.464 on 2 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.544,  Adjusted R-squared:  -0.5959 
## F-statistic: 0.4773 on 5 and 2 DF,  p-value: 0.7817

Eng>BSL interpreting

( terp_e2b_mdl <- lm(terp_e2b ~ nback_spat + 
                                    mr3d_sats + 
                                    corsi_corr +
                                    dspan_corr,
                                    data = apt) )
## 
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr, data = apt)
## 
## Coefficients:
## (Intercept)   nback_spat    mr3d_sats   corsi_corr   dspan_corr  
##    -59.9517     103.3147       0.6054       3.6444      16.8779
summary(terp_e2b_mdl)
## 
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr, data = apt)
## 
## Residuals:
##      114      116      118      119      121      122      123      128 
## -18.1905   0.1467  -1.0444  -0.1064 -13.8652   0.3798  15.2756   6.6704 
##      130 
##  10.7341 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -59.9517   117.6982  -0.509    0.637
## nback_spat  103.3147   154.9915   0.667    0.542
## mr3d_sats     0.6054     5.7021   0.106    0.921
## corsi_corr    3.6444     5.0795   0.717    0.513
## dspan_corr   16.8779   144.8769   0.116    0.913
## 
## Residual standard error: 15.14 on 4 degrees of freedom
##   (123 observations deleted due to missingness)
## Multiple R-squared:  0.2671, Adjusted R-squared:  -0.4657 
## F-statistic: 0.3645 on 4 and 4 DF,  p-value: 0.824
# but is only being fit on 10 observations.....

# wide version - pre-degree
( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~ 
                                  `nback_comb_pre-degree` + 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `dspan_corr_pre-degree`,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`, 
##     data = apt_wide)
## 
## Coefficients:
##             (Intercept)  `nback_comb_pre-degree`   `mr3d_sats_pre-degree`  
##                -154.813                  279.382                    3.565  
## `corsi_corr_pre-degree`  `dspan_corr_pre-degree`  
##                   5.672                  -46.350
summary(terp_e2b_mdl_2)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`, 
##     data = apt_wide)
## 
## Residuals:
##      15      19      22      24      29      31 
## -17.000  -3.816   2.087  12.466   1.069   5.194 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)             -154.813    672.699  -0.230    0.856
## `nback_comb_pre-degree`  279.382    976.446   0.286    0.823
## `mr3d_sats_pre-degree`     3.565      8.503   0.419    0.747
## `corsi_corr_pre-degree`    5.672      9.890   0.573    0.669
## `dspan_corr_pre-degree`  -46.350    221.553  -0.209    0.869
## 
## Residual standard error: 22.17 on 1 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.5966, Adjusted R-squared:  -1.017 
## F-statistic: 0.3697 on 4 and 1 DF,  p-value: 0.8246
( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~ 
                                  `mr3d_sats_pre-degree`,
                                  data = apt_wide) )
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree`, data = apt_wide)
## 
## Coefficients:
##            (Intercept)  `mr3d_sats_pre-degree`  
##                 66.835                   5.713
# final assessments in 3rd year
( terp_e2b_mdl_3 <- lm(`terp_e2b_3rd year` ~ 
                                  `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                  `dspan_corr_3rd year`,
                                  data = apt_wide) ) 
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`, 
##     data = apt_wide)
## 
## Coefficients:
##           (Intercept)  `nback_comb_3rd year`   `mr3d_sats_3rd year`  
##              -124.291                242.528                 -7.141  
##  `mr2d_sats_3rd year`  `corsi_corr_3rd year`  `dspan_corr_3rd year`  
##                21.894                  7.737                -75.724
summary(terp_e2b_mdl_3)
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`, 
##     data = apt_wide)
## 
## Residuals:
##       15       17       19       20       22       23       24       29 
##  -0.2739  -3.3984   4.2562   3.2719  -3.5527 -11.1615   6.4241   2.0752 
##       31 
##   2.3591 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -124.291     80.224  -1.549   0.2191  
## `nback_comb_3rd year`  242.528    130.205   1.863   0.1594  
## `mr3d_sats_3rd year`    -7.141      4.462  -1.600   0.2079  
## `mr2d_sats_3rd year`    21.894      7.566   2.894   0.0628 .
## `corsi_corr_3rd year`    7.737      3.239   2.389   0.0968 .
## `dspan_corr_3rd year`  -75.724     94.760  -0.799   0.4826  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.733 on 3 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.8172, Adjusted R-squared:  0.5127 
## F-statistic: 2.683 on 5 and 3 DF,  p-value: 0.2231

Factor Analysis

reduce tibble to just independent variables:

# apt <- select(apt, -session, -group, -ppt, -copy_score, -srt_score, -town_map, -bsl_grade)

filter out rows with incomplete data

#apt_reduced <- filter(apt, corsi_corr != "na")
#apt_reduced <- filter(apt_reduced, nback_spat != "na")
#apt_reduced <- filter(apt_reduced, kirk_ceil != "na")

reduce to just four tasks that were done at all 3 sessions

#apt_reduced <- select(apt_reduced, nback_lett:kbit_acc)

FA using psych package

# fapsych <- fa(apt_reduced, nfactors=3, rotate="oblimin", scores="regression", residuals=FALSE, fm="minres")

run factor analysis

# FA <- factanal(apt_reduced, factors=2, rotation="varimax", scores="regression")